Overview

Dataset statistics

Number of variables34
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory318.9 KiB
Average record size in memory272.1 B

Variable types

Numeric15
Categorical17
Boolean2

Alerts

Over18 has constant value ""Constant
StandardHours has constant value ""Constant
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
YearsAtCompany is highly overall correlated with YearsInCurrentRoleHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompanyHigh correlation
Department is highly overall correlated with JobRoleHigh correlation
JobRole is highly overall correlated with DepartmentHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
PerformanceRating is highly imbalanced (50.0%)Imbalance
id has unique valuesUnique
DailyRate has unique valuesUnique
DistanceFromHome has unique valuesUnique
NumCompaniesWorked has 167 (13.9%) zerosZeros
TrainingTimesLastYear has 53 (4.4%) zerosZeros
YearsAtCompany has 23 (1.9%) zerosZeros
YearsInCurrentRole has 208 (17.3%) zerosZeros
YearsSinceLastPromotion has 499 (41.6%) zerosZeros
YearsWithCurrManager has 239 (19.9%) zerosZeros

Reproduction

Analysis started2023-07-01 12:18:27.123608
Analysis finished2023-07-01 12:18:50.748465
Duration23.62 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.0758
Minimum0
Maximum1998
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:50.804332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108.85
Q1508.75
median1018
Q31519.25
95-th percentile1907.05
Maximum1998
Range1998
Interquartile range (IQR)1010.5

Descriptive statistics

Standard deviation575.72604
Coefficient of variation (CV)0.5682951
Kurtosis-1.2027504
Mean1013.0758
Median Absolute Deviation (MAD)506
Skewness-0.024972633
Sum1215691
Variance331460.47
MonotonicityStrictly increasing
2023-07-01T12:18:50.908892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
1365 1
 
0.1%
1360 1
 
0.1%
1354 1
 
0.1%
1353 1
 
0.1%
1352 1
 
0.1%
1351 1
 
0.1%
1350 1
 
0.1%
1349 1
 
0.1%
1348 1
 
0.1%
Other values (1190) 1190
99.2%
ValueCountFrequency (%)
0 1
0.1%
3 1
0.1%
7 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
15 1
0.1%
20 1
0.1%
21 1
0.1%
23 1
0.1%
ValueCountFrequency (%)
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1994 1
0.1%
1988 1
0.1%
1987 1
0.1%
1986 1
0.1%
1984 1
0.1%
1980 1
0.1%
1976 1
0.1%

Age
Real number (ℝ)

Distinct36
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.701667
Minimum17
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:51.014433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q126
median34
Q337
95-th percentile47
Maximum56
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.2821994
Coefficient of variation (CV)0.25326536
Kurtosis-0.51904912
Mean32.701667
Median Absolute Deviation (MAD)7
Skewness0.066480782
Sum39242
Variance68.594826
MonotonicityNot monotonic
2023-07-01T12:18:51.104159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
37 221
18.4%
26 159
13.2%
34 146
12.2%
25 125
10.4%
35 67
 
5.6%
27 59
 
4.9%
17 50
 
4.2%
46 48
 
4.0%
18 46
 
3.8%
45 39
 
3.2%
Other values (26) 240
20.0%
ValueCountFrequency (%)
17 50
 
4.2%
18 46
 
3.8%
19 6
 
0.5%
20 3
 
0.2%
22 2
 
0.2%
25 125
10.4%
26 159
13.2%
27 59
 
4.9%
28 20
 
1.7%
29 12
 
1.0%
ValueCountFrequency (%)
56 1
 
0.1%
55 1
 
0.1%
53 3
 
0.2%
52 3
 
0.2%
51 2
 
0.2%
50 7
 
0.6%
49 7
 
0.6%
48 16
 
1.3%
47 28
2.3%
46 48
4.0%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Travel_Rarely
808 
Travel_Frequently
263 
Non-Travel
129 

Length

Max length17
Median length13
Mean length13.554167
Min length10

Characters and Unicode

Total characters16265
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 808
67.3%
Travel_Frequently 263
 
21.9%
Non-Travel 129
 
10.8%

Length

2023-07-01T12:18:51.198431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:51.291513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 808
67.3%
travel_frequently 263
 
21.9%
non-travel 129
 
10.8%

Most occurring characters

ValueCountFrequency (%)
e 2534
15.6%
r 2271
14.0%
l 2271
14.0%
a 2008
12.3%
T 1200
7.4%
v 1200
7.4%
y 1071
6.6%
_ 1071
6.6%
R 808
 
5.0%
n 392
 
2.4%
Other values (7) 1439
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12665
77.9%
Uppercase Letter 2400
 
14.8%
Connector Punctuation 1071
 
6.6%
Dash Punctuation 129
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2534
20.0%
r 2271
17.9%
l 2271
17.9%
a 2008
15.9%
v 1200
9.5%
y 1071
8.5%
n 392
 
3.1%
q 263
 
2.1%
u 263
 
2.1%
t 263
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
T 1200
50.0%
R 808
33.7%
F 263
 
11.0%
N 129
 
5.4%
Connector Punctuation
ValueCountFrequency (%)
_ 1071
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15065
92.6%
Common 1200
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2534
16.8%
r 2271
15.1%
l 2271
15.1%
a 2008
13.3%
T 1200
8.0%
v 1200
8.0%
y 1071
7.1%
R 808
 
5.4%
n 392
 
2.6%
F 263
 
1.7%
Other values (5) 1047
6.9%
Common
ValueCountFrequency (%)
_ 1071
89.2%
- 129
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2534
15.6%
r 2271
14.0%
l 2271
14.0%
a 2008
12.3%
T 1200
7.4%
v 1200
7.4%
y 1071
6.6%
_ 1071
6.6%
R 808
 
5.0%
n 392
 
2.4%
Other values (7) 1439
8.8%

DailyRate
Real number (ℝ)

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750.75416
Minimum59.231581
Maximum1484.9793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:51.375572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum59.231581
5-th percentile83.848354
Q1403.94604
median736.87346
Q31092.4435
95-th percentile1365.1237
Maximum1484.9793
Range1425.7477
Interquartile range (IQR)688.49745

Descriptive statistics

Standard deviation415.42092
Coefficient of variation (CV)0.55333815
Kurtosis-1.1789886
Mean750.75416
Median Absolute Deviation (MAD)339.74135
Skewness-0.067427044
Sum900904.99
Variance172574.54
MonotonicityNot monotonic
2023-07-01T12:18:51.473468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450.9414761 1
 
0.1%
1233.337111 1
 
0.1%
664.2526387 1
 
0.1%
457.5017384 1
 
0.1%
1256.236434 1
 
0.1%
1015.368774 1
 
0.1%
65.94567025 1
 
0.1%
992.8258237 1
 
0.1%
360.6902588 1
 
0.1%
677.2819696 1
 
0.1%
Other values (1190) 1190
99.2%
ValueCountFrequency (%)
59.23158071 1
0.1%
59.51664581 1
0.1%
59.5956616 1
0.1%
60.15841398 1
0.1%
61.27188898 1
0.1%
61.66784652 1
0.1%
61.773276 1
0.1%
61.96507656 1
0.1%
62.1432704 1
0.1%
63.10039212 1
0.1%
ValueCountFrequency (%)
1484.979305 1
0.1%
1474.733189 1
0.1%
1474.057242 1
0.1%
1469.318486 1
0.1%
1462.741451 1
0.1%
1460.281685 1
0.1%
1458.515327 1
0.1%
1456.724987 1
0.1%
1456.489188 1
0.1%
1445.356537 1
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Research & Development
773 
Sales
381 
Human Resources
 
46

Length

Max length22
Median length22
Mean length16.334167
Min length5

Characters and Unicode

Total characters19601
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowHuman Resources
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 773
64.4%
Sales 381
31.8%
Human Resources 46
 
3.8%

Length

2023-07-01T12:18:51.567750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:51.660178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
research 773
27.7%
773
27.7%
development 773
27.7%
sales 381
13.6%
human 46
 
1.6%
resources 46
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 4338
22.1%
1592
 
8.1%
s 1246
 
6.4%
a 1200
 
6.1%
l 1154
 
5.9%
R 819
 
4.2%
r 819
 
4.2%
c 819
 
4.2%
n 819
 
4.2%
m 819
 
4.2%
Other values (10) 5976
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15217
77.6%
Uppercase Letter 2019
 
10.3%
Space Separator 1592
 
8.1%
Other Punctuation 773
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4338
28.5%
s 1246
 
8.2%
a 1200
 
7.9%
l 1154
 
7.6%
r 819
 
5.4%
c 819
 
5.4%
n 819
 
5.4%
m 819
 
5.4%
o 819
 
5.4%
p 773
 
5.1%
Other values (4) 2411
15.8%
Uppercase Letter
ValueCountFrequency (%)
R 819
40.6%
D 773
38.3%
S 381
18.9%
H 46
 
2.3%
Space Separator
ValueCountFrequency (%)
1592
100.0%
Other Punctuation
ValueCountFrequency (%)
& 773
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17236
87.9%
Common 2365
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4338
25.2%
s 1246
 
7.2%
a 1200
 
7.0%
l 1154
 
6.7%
R 819
 
4.8%
r 819
 
4.8%
c 819
 
4.8%
n 819
 
4.8%
m 819
 
4.8%
o 819
 
4.8%
Other values (8) 4384
25.4%
Common
ValueCountFrequency (%)
1592
67.3%
& 773
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4338
22.1%
1592
 
8.1%
s 1246
 
6.4%
a 1200
 
6.1%
l 1154
 
5.9%
R 819
 
4.2%
r 819
 
4.2%
c 819
 
4.2%
n 819
 
4.2%
m 819
 
4.2%
Other values (10) 5976
30.5%

DistanceFromHome
Real number (ℝ)

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.218387
Minimum-0.023998944
Maximum29.890208
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size9.5 KiB
2023-07-01T12:18:51.743797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.023998944
5-th percentile0.92044839
Q12.6881729
median9.3459236
Q315.231438
95-th percentile26.819042
Maximum29.890208
Range29.914207
Interquartile range (IQR)12.543265

Descriptive statistics

Standard deviation8.1341436
Coefficient of variation (CV)0.79603011
Kurtosis-0.4615941
Mean10.218387
Median Absolute Deviation (MAD)6.5313087
Skewness0.76713422
Sum12262.064
Variance66.164293
MonotonicityNot monotonic
2023-07-01T12:18:51.839001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.601074061 1
 
0.1%
2.590428227 1
 
0.1%
2.244686601 1
 
0.1%
6.63874627 1
 
0.1%
21.85243931 1
 
0.1%
0.607026169 1
 
0.1%
29.12177233 1
 
0.1%
3.370938412 1
 
0.1%
1.232529216 1
 
0.1%
1.843888398 1
 
0.1%
Other values (1190) 1190
99.2%
ValueCountFrequency (%)
-0.023998944 1
0.1%
0.044009304 1
0.1%
0.096660897 1
0.1%
0.126019874 1
0.1%
0.12913472 1
0.1%
0.188990624 1
0.1%
0.257068377 1
0.1%
0.259836294 1
0.1%
0.305059682 1
0.1%
0.322637514 1
0.1%
ValueCountFrequency (%)
29.89020821 1
0.1%
29.798857 1
0.1%
29.772229 1
0.1%
29.42570635 1
0.1%
29.39117084 1
0.1%
29.27060578 1
0.1%
29.24822627 1
0.1%
29.22363828 1
0.1%
29.14726065 1
0.1%
29.12177233 1
0.1%

Education
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
447 
4
326 
2
220 
1
174 
5
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row4

Common Values

ValueCountFrequency (%)
3 447
37.2%
4 326
27.2%
2 220
18.3%
1 174
 
14.5%
5 33
 
2.8%

Length

2023-07-01T12:18:51.928675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:52.015644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 447
37.2%
4 326
27.2%
2 220
18.3%
1 174
 
14.5%
5 33
 
2.8%

Most occurring characters

ValueCountFrequency (%)
3 447
37.2%
4 326
27.2%
2 220
18.3%
1 174
 
14.5%
5 33
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 447
37.2%
4 326
27.2%
2 220
18.3%
1 174
 
14.5%
5 33
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 447
37.2%
4 326
27.2%
2 220
18.3%
1 174
 
14.5%
5 33
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 447
37.2%
4 326
27.2%
2 220
18.3%
1 174
 
14.5%
5 33
 
2.8%

EducationField
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Life Sciences
501 
Medical
334 
Marketing
154 
Technical Degree
98 
Other
97 

Length

Max length16
Median length15
Mean length10.441667
Min length5

Characters and Unicode

Total characters12530
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedical
2nd rowTechnical Degree
3rd rowLife Sciences
4th rowMedical
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 501
41.8%
Medical 334
27.8%
Marketing 154
 
12.8%
Technical Degree 98
 
8.2%
Other 97
 
8.1%
Human Resources 16
 
1.3%

Length

2023-07-01T12:18:52.099555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:52.194128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
life 501
27.6%
sciences 501
27.6%
medical 334
18.4%
marketing 154
 
8.5%
technical 98
 
5.4%
degree 98
 
5.4%
other 97
 
5.3%
human 16
 
0.9%
resources 16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 2512
20.0%
i 1588
12.7%
c 1548
12.4%
n 769
 
6.1%
615
 
4.9%
a 602
 
4.8%
s 533
 
4.3%
L 501
 
4.0%
f 501
 
4.0%
S 501
 
4.0%
Other values (16) 2860
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10100
80.6%
Uppercase Letter 1815
 
14.5%
Space Separator 615
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2512
24.9%
i 1588
15.7%
c 1548
15.3%
n 769
 
7.6%
a 602
 
6.0%
s 533
 
5.3%
f 501
 
5.0%
l 432
 
4.3%
r 365
 
3.6%
d 334
 
3.3%
Other values (7) 916
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
L 501
27.6%
S 501
27.6%
M 488
26.9%
T 98
 
5.4%
D 98
 
5.4%
O 97
 
5.3%
H 16
 
0.9%
R 16
 
0.9%
Space Separator
ValueCountFrequency (%)
615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11915
95.1%
Common 615
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2512
21.1%
i 1588
13.3%
c 1548
13.0%
n 769
 
6.5%
a 602
 
5.1%
s 533
 
4.5%
L 501
 
4.2%
f 501
 
4.2%
S 501
 
4.2%
M 488
 
4.1%
Other values (15) 2372
19.9%
Common
ValueCountFrequency (%)
615
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2512
20.0%
i 1588
12.7%
c 1548
12.4%
n 769
 
6.1%
615
 
4.9%
a 602
 
4.8%
s 533
 
4.3%
L 501
 
4.0%
f 501
 
4.0%
S 501
 
4.0%
Other values (16) 2860
22.8%

EmployeeNumber
Real number (ℝ)

Distinct165
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1077.855
Minimum12
Maximum2060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:52.293381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile60.95
Q1699
median1059
Q31591
95-th percentile1862
Maximum2060
Range2048
Interquartile range (IQR)892

Descriptive statistics

Standard deviation580.18549
Coefficient of variation (CV)0.53827787
Kurtosis-0.97833826
Mean1077.855
Median Absolute Deviation (MAD)528
Skewness-0.30385413
Sum1293426
Variance336615.21
MonotonicityNot monotonic
2023-07-01T12:18:52.388227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
975 106
 
8.8%
1587 73
 
6.1%
1719 66
 
5.5%
1138 62
 
5.2%
1591 58
 
4.8%
1862 46
 
3.8%
28 39
 
3.2%
699 28
 
2.3%
1291 26
 
2.2%
120 22
 
1.8%
Other values (155) 674
56.2%
ValueCountFrequency (%)
12 1
 
0.1%
15 5
 
0.4%
28 39
3.2%
35 2
 
0.2%
42 1
 
0.1%
46 1
 
0.1%
54 5
 
0.4%
55 1
 
0.1%
60 5
 
0.4%
61 6
 
0.5%
ValueCountFrequency (%)
2060 5
0.4%
2048 7
0.6%
2027 7
0.6%
2022 3
0.2%
1999 2
 
0.2%
1996 3
0.2%
1995 6
0.5%
1968 1
 
0.1%
1948 1
 
0.1%
1929 2
 
0.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
4
368 
3
326 
1
275 
2
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 368
30.7%
3 326
27.2%
1 275
22.9%
2 231
19.2%

Length

2023-07-01T12:18:52.474430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:52.558205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 368
30.7%
3 326
27.2%
1 275
22.9%
2 231
19.2%

Most occurring characters

ValueCountFrequency (%)
4 368
30.7%
3 326
27.2%
1 275
22.9%
2 231
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 368
30.7%
3 326
27.2%
1 275
22.9%
2 231
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 368
30.7%
3 326
27.2%
1 275
22.9%
2 231
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 368
30.7%
3 326
27.2%
1 275
22.9%
2 231
19.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Male
713 
Female
487 

Length

Max length6
Median length4
Mean length4.8116667
Min length4

Characters and Unicode

Total characters5774
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 713
59.4%
Female 487
40.6%

Length

2023-07-01T12:18:52.641271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:52.730665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 713
59.4%
female 487
40.6%

Most occurring characters

ValueCountFrequency (%)
e 1687
29.2%
a 1200
20.8%
l 1200
20.8%
M 713
12.3%
F 487
 
8.4%
m 487
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4574
79.2%
Uppercase Letter 1200
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1687
36.9%
a 1200
26.2%
l 1200
26.2%
m 487
 
10.6%
Uppercase Letter
ValueCountFrequency (%)
M 713
59.4%
F 487
40.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5774
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1687
29.2%
a 1200
20.8%
l 1200
20.8%
M 713
12.3%
F 487
 
8.4%
m 487
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1687
29.2%
a 1200
20.8%
l 1200
20.8%
M 713
12.3%
F 487
 
8.4%
m 487
 
8.4%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.261667
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:53.282272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile34
Q148
median67
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.257801
Coefficient of variation (CV)0.30572429
Kurtosis-1.2315589
Mean66.261667
Median Absolute Deviation (MAD)18
Skewness-0.070535817
Sum79514
Variance410.37851
MonotonicityNot monotonic
2023-07-01T12:18:53.381042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 52
 
4.3%
45 49
 
4.1%
98 41
 
3.4%
85 34
 
2.8%
74 33
 
2.8%
79 33
 
2.8%
78 29
 
2.4%
61 27
 
2.2%
30 26
 
2.2%
56 25
 
2.1%
Other values (61) 851
70.9%
ValueCountFrequency (%)
30 26
2.2%
31 11
0.9%
32 17
1.4%
33 4
 
0.3%
34 7
 
0.6%
35 5
 
0.4%
36 8
 
0.7%
37 23
1.9%
38 7
 
0.6%
39 22
1.8%
ValueCountFrequency (%)
100 10
 
0.8%
99 5
 
0.4%
98 41
3.4%
97 24
2.0%
96 7
 
0.6%
95 21
1.8%
94 22
1.8%
93 8
 
0.7%
92 21
1.8%
91 16
 
1.3%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
709 
2
339 
4
101 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 709
59.1%
2 339
28.2%
4 101
 
8.4%
1 51
 
4.2%

Length

2023-07-01T12:18:53.474790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:53.558442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 709
59.1%
2 339
28.2%
4 101
 
8.4%
1 51
 
4.2%

Most occurring characters

ValueCountFrequency (%)
3 709
59.1%
2 339
28.2%
4 101
 
8.4%
1 51
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 709
59.1%
2 339
28.2%
4 101
 
8.4%
1 51
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 709
59.1%
2 339
28.2%
4 101
 
8.4%
1 51
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 709
59.1%
2 339
28.2%
4 101
 
8.4%
1 51
 
4.2%

JobLevel
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1
462 
2
377 
3
172 
4
126 
5
63 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 462
38.5%
2 377
31.4%
3 172
 
14.3%
4 126
 
10.5%
5 63
 
5.2%

Length

2023-07-01T12:18:53.631638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:53.718200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 462
38.5%
2 377
31.4%
3 172
 
14.3%
4 126
 
10.5%
5 63
 
5.2%

Most occurring characters

ValueCountFrequency (%)
1 462
38.5%
2 377
31.4%
3 172
 
14.3%
4 126
 
10.5%
5 63
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 462
38.5%
2 377
31.4%
3 172
 
14.3%
4 126
 
10.5%
5 63
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 462
38.5%
2 377
31.4%
3 172
 
14.3%
4 126
 
10.5%
5 63
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 462
38.5%
2 377
31.4%
3 172
 
14.3%
4 126
 
10.5%
5 63
 
5.2%

JobRole
Categorical

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Research Scientist
252 
Sales Executive
249 
Laboratory Technician
179 
Manufacturing Director
129 
Sales Representative
101 
Other values (4)
290 

Length

Max length25
Median length21
Mean length18.115
Min length7

Characters and Unicode

Total characters21738
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowResearch Scientist
3rd rowHuman Resources
4th rowLaboratory Technician
5th rowManufacturing Director

Common Values

ValueCountFrequency (%)
Research Scientist 252
21.0%
Sales Executive 249
20.8%
Laboratory Technician 179
14.9%
Manufacturing Director 129
10.8%
Sales Representative 101
8.4%
Healthcare Representative 92
 
7.7%
Research Director 90
 
7.5%
Manager 75
 
6.2%
Human Resources 33
 
2.8%

Length

2023-07-01T12:18:53.805475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:53.913194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sales 350
15.1%
research 342
14.7%
scientist 252
10.8%
executive 249
10.7%
director 219
9.4%
representative 193
8.3%
laboratory 179
7.7%
technician 179
7.7%
manufacturing 129
 
5.5%
healthcare 92
 
4.0%
Other values (3) 141
6.1%

Most occurring characters

ValueCountFrequency (%)
e 3279
15.1%
a 2047
 
9.4%
t 1758
 
8.1%
c 1674
 
7.7%
r 1660
 
7.6%
i 1652
 
7.6%
s 1203
 
5.5%
n 1169
 
5.4%
1125
 
5.2%
h 613
 
2.8%
Other values (19) 5558
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18288
84.1%
Uppercase Letter 2325
 
10.7%
Space Separator 1125
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3279
17.9%
a 2047
11.2%
t 1758
9.6%
c 1674
9.2%
r 1660
9.1%
i 1652
9.0%
s 1203
 
6.6%
n 1169
 
6.4%
h 613
 
3.4%
o 610
 
3.3%
Other values (10) 2623
14.3%
Uppercase Letter
ValueCountFrequency (%)
S 602
25.9%
R 568
24.4%
E 249
10.7%
D 219
 
9.4%
M 204
 
8.8%
L 179
 
7.7%
T 179
 
7.7%
H 125
 
5.4%
Space Separator
ValueCountFrequency (%)
1125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20613
94.8%
Common 1125
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3279
15.9%
a 2047
9.9%
t 1758
 
8.5%
c 1674
 
8.1%
r 1660
 
8.1%
i 1652
 
8.0%
s 1203
 
5.8%
n 1169
 
5.7%
h 613
 
3.0%
o 610
 
3.0%
Other values (18) 4948
24.0%
Common
ValueCountFrequency (%)
1125
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3279
15.1%
a 2047
 
9.4%
t 1758
 
8.1%
c 1674
 
7.7%
r 1660
 
7.6%
i 1652
 
7.6%
s 1203
 
5.5%
n 1169
 
5.4%
1125
 
5.2%
h 613
 
2.8%
Other values (19) 5558
25.6%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
371 
4
361 
2
236 
1
232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 371
30.9%
4 361
30.1%
2 236
19.7%
1 232
19.3%

Length

2023-07-01T12:18:54.012097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:54.096242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 371
30.9%
4 361
30.1%
2 236
19.7%
1 232
19.3%

Most occurring characters

ValueCountFrequency (%)
3 371
30.9%
4 361
30.1%
2 236
19.7%
1 232
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 371
30.9%
4 361
30.1%
2 236
19.7%
1 232
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 371
30.9%
4 361
30.1%
2 236
19.7%
1 232
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 371
30.9%
4 361
30.1%
2 236
19.7%
1 232
19.3%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Married
578 
Single
359 
Divorced
263 

Length

Max length8
Median length7
Mean length6.92
Min length6

Characters and Unicode

Total characters8304
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowDivorced
3rd rowMarried
4th rowDivorced
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married 578
48.2%
Single 359
29.9%
Divorced 263
21.9%

Length

2023-07-01T12:18:54.179575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:54.271594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 578
48.2%
single 359
29.9%
divorced 263
21.9%

Most occurring characters

ValueCountFrequency (%)
r 1419
17.1%
i 1200
14.5%
e 1200
14.5%
d 841
10.1%
M 578
7.0%
a 578
7.0%
S 359
 
4.3%
n 359
 
4.3%
g 359
 
4.3%
l 359
 
4.3%
Other values (4) 1052
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7104
85.5%
Uppercase Letter 1200
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1419
20.0%
i 1200
16.9%
e 1200
16.9%
d 841
11.8%
a 578
8.1%
n 359
 
5.1%
g 359
 
5.1%
l 359
 
5.1%
v 263
 
3.7%
o 263
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
M 578
48.2%
S 359
29.9%
D 263
21.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 8304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1419
17.1%
i 1200
14.5%
e 1200
14.5%
d 841
10.1%
M 578
7.0%
a 578
7.0%
S 359
 
4.3%
n 359
 
4.3%
g 359
 
4.3%
l 359
 
4.3%
Other values (4) 1052
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1419
17.1%
i 1200
14.5%
e 1200
14.5%
d 841
10.1%
M 578
7.0%
a 578
7.0%
S 359
 
4.3%
n 359
 
4.3%
g 359
 
4.3%
l 359
 
4.3%
Other values (4) 1052
12.7%

MonthlyIncome
Real number (ℝ)

Distinct188
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7052.5217
Minimum1052
Maximum19833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:54.355076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1052
5-th percentile2088
Q13537
median5071
Q38715.25
95-th percentile17159
Maximum19833
Range18781
Interquartile range (IQR)5178.25

Descriptive statistics

Standard deviation5033.677
Coefficient of variation (CV)0.71374145
Kurtosis0.088546111
Mean7052.5217
Median Absolute Deviation (MAD)2121
Skewness1.1672684
Sum8463026
Variance25337904
MonotonicityNot monotonic
2023-07-01T12:18:54.454310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4789 64
 
5.3%
17159 43
 
3.6%
4936 36
 
3.0%
7457 34
 
2.8%
4189 33
 
2.8%
5228 29
 
2.4%
2011 29
 
2.4%
5071 26
 
2.2%
19033 26
 
2.2%
13247 25
 
2.1%
Other values (178) 855
71.2%
ValueCountFrequency (%)
1052 1
 
0.1%
1118 4
 
0.3%
1261 1
 
0.1%
1359 1
 
0.1%
1563 6
 
0.5%
1951 1
 
0.1%
2007 1
 
0.1%
2008 2
 
0.2%
2011 29
2.4%
2024 4
 
0.3%
ValueCountFrequency (%)
19833 1
 
0.1%
19502 14
 
1.2%
19033 26
2.2%
18824 5
 
0.4%
18711 4
 
0.3%
17650 3
 
0.2%
17444 3
 
0.2%
17169 2
 
0.2%
17159 43
3.6%
17123 6
 
0.5%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6141667
Minimum0
Maximum9
Zeros167
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:54.545386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4604222
Coefficient of variation (CV)0.94118796
Kurtosis-0.059750024
Mean2.6141667
Median Absolute Deviation (MAD)1
Skewness1.0346973
Sum3137
Variance6.0536774
MonotonicityNot monotonic
2023-07-01T12:18:54.613147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 438
36.5%
0 167
 
13.9%
3 145
 
12.1%
2 117
 
9.8%
4 87
 
7.2%
6 67
 
5.6%
8 63
 
5.2%
7 58
 
4.8%
5 38
 
3.2%
9 20
 
1.7%
ValueCountFrequency (%)
0 167
 
13.9%
1 438
36.5%
2 117
 
9.8%
3 145
 
12.1%
4 87
 
7.2%
5 38
 
3.2%
6 67
 
5.6%
7 58
 
4.8%
8 63
 
5.2%
9 20
 
1.7%
ValueCountFrequency (%)
9 20
 
1.7%
8 63
 
5.2%
7 58
 
4.8%
6 67
 
5.6%
5 38
 
3.2%
4 87
 
7.2%
3 145
 
12.1%
2 117
 
9.8%
1 438
36.5%
0 167
 
13.9%

Over18
Boolean

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
1200 
ValueCountFrequency (%)
True 1200
100.0%
2023-07-01T12:18:54.689471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
882 
True
318 
ValueCountFrequency (%)
False 882
73.5%
True 318
 
26.5%
2023-07-01T12:18:54.755695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.115
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:54.818093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6324148
Coefficient of variation (CV)0.24031854
Kurtosis-0.44653283
Mean15.115
Median Absolute Deviation (MAD)2
Skewness0.78962221
Sum18138
Variance13.194437
MonotonicityNot monotonic
2023-07-01T12:18:54.890179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13 185
15.4%
12 182
15.2%
11 173
14.4%
14 161
13.4%
19 83
6.9%
18 76
6.3%
16 67
 
5.6%
20 58
 
4.8%
17 54
 
4.5%
22 51
 
4.2%
Other values (5) 110
9.2%
ValueCountFrequency (%)
11 173
14.4%
12 182
15.2%
13 185
15.4%
14 161
13.4%
15 44
 
3.7%
16 67
 
5.6%
17 54
 
4.5%
18 76
6.3%
19 83
6.9%
20 58
 
4.8%
ValueCountFrequency (%)
25 8
 
0.7%
24 13
 
1.1%
23 29
 
2.4%
22 51
4.2%
21 16
 
1.3%
20 58
4.8%
19 83
6.9%
18 76
6.3%
17 54
4.5%
16 67
5.6%

PerformanceRating
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
1068 
4
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 1068
89.0%
4 132
 
11.0%

Length

2023-07-01T12:18:54.974516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:55.053026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1068
89.0%
4 132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
3 1068
89.0%
4 132
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1068
89.0%
4 132
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1068
89.0%
4 132
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1068
89.0%
4 132
 
11.0%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
380 
4
300 
2
280 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
3 380
31.7%
4 300
25.0%
2 280
23.3%
1 240
20.0%

Length

2023-07-01T12:18:55.120735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:55.206926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 380
31.7%
4 300
25.0%
2 280
23.3%
1 240
20.0%

Most occurring characters

ValueCountFrequency (%)
3 380
31.7%
4 300
25.0%
2 280
23.3%
1 240
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 380
31.7%
4 300
25.0%
2 280
23.3%
1 240
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 380
31.7%
4 300
25.0%
2 280
23.3%
1 240
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 380
31.7%
4 300
25.0%
2 280
23.3%
1 240
20.0%

StandardHours
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
80
1200 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
80 1200
100.0%

Length

2023-07-01T12:18:55.283749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:55.361333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
80 1200
100.0%

Most occurring characters

ValueCountFrequency (%)
8 1200
50.0%
0 1200
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 1200
50.0%
0 1200
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 1200
50.0%
0 1200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1200
50.0%
0 1200
50.0%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0
480 
1
476 
2
172 
3
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 480
40.0%
1 476
39.7%
2 172
 
14.3%
3 72
 
6.0%

Length

2023-07-01T12:18:55.425456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:55.510200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 480
40.0%
1 476
39.7%
2 172
 
14.3%
3 72
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 480
40.0%
1 476
39.7%
2 172
 
14.3%
3 72
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 480
40.0%
1 476
39.7%
2 172
 
14.3%
3 72
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 480
40.0%
1 476
39.7%
2 172
 
14.3%
3 72
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 480
40.0%
1 476
39.7%
2 172
 
14.3%
3 72
 
6.0%

TotalWorkingYears
Real number (ℝ)

Distinct36
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.631667
Minimum0
Maximum36
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:55.591532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q315
95-th percentile26
Maximum36
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7213349
Coefficient of variation (CV)0.72625818
Kurtosis0.55153352
Mean10.631667
Median Absolute Deviation (MAD)4
Skewness1.0222732
Sum12758
Variance59.619013
MonotonicityNot monotonic
2023-07-01T12:18:55.683299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10 189
15.8%
6 120
 
10.0%
1 97
 
8.1%
9 67
 
5.6%
4 64
 
5.3%
5 62
 
5.2%
16 54
 
4.5%
8 53
 
4.4%
2 50
 
4.2%
7 43
 
3.6%
Other values (26) 401
33.4%
ValueCountFrequency (%)
0 7
 
0.6%
1 97
8.1%
2 50
4.2%
3 39
 
3.2%
4 64
5.3%
5 62
5.2%
6 120
10.0%
7 43
 
3.6%
8 53
4.4%
9 67
5.6%
ValueCountFrequency (%)
36 2
 
0.2%
34 1
 
0.1%
33 11
0.9%
32 9
0.8%
31 20
1.7%
30 1
 
0.1%
29 5
 
0.4%
28 2
 
0.2%
27 4
 
0.3%
26 14
1.2%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.755
Minimum0
Maximum6
Zeros53
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:55.768737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2561674
Coefficient of variation (CV)0.45595914
Kurtosis0.50016086
Mean2.755
Median Absolute Deviation (MAD)1
Skewness0.39628152
Sum3306
Variance1.5779566
MonotonicityNot monotonic
2023-07-01T12:18:55.835441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 435
36.2%
3 411
34.2%
4 109
 
9.1%
5 100
 
8.3%
1 57
 
4.8%
0 53
 
4.4%
6 35
 
2.9%
ValueCountFrequency (%)
0 53
 
4.4%
1 57
 
4.8%
2 435
36.2%
3 411
34.2%
4 109
 
9.1%
5 100
 
8.3%
6 35
 
2.9%
ValueCountFrequency (%)
6 35
 
2.9%
5 100
 
8.3%
4 109
 
9.1%
3 411
34.2%
2 435
36.2%
1 57
 
4.8%
0 53
 
4.4%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
707 
2
323 
4
101 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 707
58.9%
2 323
26.9%
4 101
 
8.4%
1 69
 
5.8%

Length

2023-07-01T12:18:55.914310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:55.998629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 707
58.9%
2 323
26.9%
4 101
 
8.4%
1 69
 
5.8%

Most occurring characters

ValueCountFrequency (%)
3 707
58.9%
2 323
26.9%
4 101
 
8.4%
1 69
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 707
58.9%
2 323
26.9%
4 101
 
8.4%
1 69
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 707
58.9%
2 323
26.9%
4 101
 
8.4%
1 69
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 707
58.9%
2 323
26.9%
4 101
 
8.4%
1 69
 
5.8%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3516667
Minimum0
Maximum32
Zeros23
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:56.074811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q39
95-th percentile18
Maximum32
Range32
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1205319
Coefficient of variation (CV)0.80617139
Kurtosis2.4597397
Mean6.3516667
Median Absolute Deviation (MAD)3
Skewness1.4090764
Sum7622
Variance26.219847
MonotonicityNot monotonic
2023-07-01T12:18:56.157505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 164
13.7%
5 164
13.7%
2 126
10.5%
10 118
9.8%
3 110
9.2%
6 79
6.6%
4 77
 
6.4%
9 64
 
5.3%
7 53
 
4.4%
8 50
 
4.2%
Other values (17) 195
16.2%
ValueCountFrequency (%)
0 23
 
1.9%
1 164
13.7%
2 126
10.5%
3 110
9.2%
4 77
6.4%
5 164
13.7%
6 79
6.6%
7 53
 
4.4%
8 50
 
4.2%
9 64
 
5.3%
ValueCountFrequency (%)
32 1
 
0.1%
31 2
 
0.2%
26 4
 
0.3%
25 3
 
0.2%
22 2
 
0.2%
21 12
1.0%
20 22
1.8%
19 8
 
0.7%
18 7
 
0.6%
17 5
 
0.4%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2491667
Minimum0
Maximum17
Zeros208
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:56.241128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6885074
Coefficient of variation (CV)0.8680543
Kurtosis-0.2906384
Mean4.2491667
Median Absolute Deviation (MAD)3
Skewness0.76377551
Sum5099
Variance13.605087
MonotonicityNot monotonic
2023-07-01T12:18:56.317611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 329
27.4%
0 208
17.3%
7 135
11.2%
3 91
 
7.6%
9 77
 
6.4%
4 77
 
6.4%
8 74
 
6.2%
10 50
 
4.2%
1 46
 
3.8%
6 28
 
2.3%
Other values (8) 85
 
7.1%
ValueCountFrequency (%)
0 208
17.3%
1 46
 
3.8%
2 329
27.4%
3 91
 
7.6%
4 77
 
6.4%
5 21
 
1.8%
6 28
 
2.3%
7 135
11.2%
8 74
 
6.2%
9 77
 
6.4%
ValueCountFrequency (%)
17 1
 
0.1%
16 4
 
0.3%
15 6
 
0.5%
14 3
 
0.2%
13 20
 
1.7%
12 8
 
0.7%
11 22
 
1.8%
10 50
4.2%
9 77
6.4%
8 74
6.2%

YearsSinceLastPromotion
Real number (ℝ)

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1633333
Minimum0
Maximum15
Zeros499
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:56.400992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.3122195
Coefficient of variation (CV)1.5310722
Kurtosis3.060314
Mean2.1633333
Median Absolute Deviation (MAD)1
Skewness1.9229995
Sum2596
Variance10.970798
MonotonicityNot monotonic
2023-07-01T12:18:56.476940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 499
41.6%
1 305
25.4%
2 121
 
10.1%
7 54
 
4.5%
5 45
 
3.8%
8 36
 
3.0%
11 34
 
2.8%
3 26
 
2.2%
4 21
 
1.8%
6 15
 
1.2%
Other values (6) 44
 
3.7%
ValueCountFrequency (%)
0 499
41.6%
1 305
25.4%
2 121
 
10.1%
3 26
 
2.2%
4 21
 
1.8%
5 45
 
3.8%
6 15
 
1.2%
7 54
 
4.5%
8 36
 
3.0%
9 11
 
0.9%
ValueCountFrequency (%)
15 12
 
1.0%
14 3
 
0.2%
13 7
 
0.6%
12 5
 
0.4%
11 34
2.8%
10 6
 
0.5%
9 11
 
0.9%
8 36
3.0%
7 54
4.5%
6 15
 
1.2%

YearsWithCurrManager
Real number (ℝ)

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.78
Minimum0
Maximum17
Zeros239
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-01T12:18:56.558506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile9
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3142249
Coefficient of variation (CV)0.87677908
Kurtosis-0.2729007
Mean3.78
Median Absolute Deviation (MAD)3
Skewness0.75411763
Sum4536
Variance10.984087
MonotonicityNot monotonic
2023-07-01T12:18:56.633643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 306
25.5%
0 239
19.9%
7 154
12.8%
3 124
10.3%
8 95
 
7.9%
4 89
 
7.4%
9 71
 
5.9%
1 52
 
4.3%
11 16
 
1.3%
5 14
 
1.2%
Other values (6) 40
 
3.3%
ValueCountFrequency (%)
0 239
19.9%
1 52
 
4.3%
2 306
25.5%
3 124
10.3%
4 89
 
7.4%
5 14
 
1.2%
6 11
 
0.9%
7 154
12.8%
8 95
 
7.9%
9 71
 
5.9%
ValueCountFrequency (%)
17 2
 
0.2%
14 1
 
0.1%
13 11
 
0.9%
12 7
 
0.6%
11 16
 
1.3%
10 8
 
0.7%
9 71
5.9%
8 95
7.9%
7 154
12.8%
6 11
 
0.9%

Attrition
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0
987 
1
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 987
82.2%
1 213
 
17.8%

Length

2023-07-01T12:18:56.718807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T12:18:56.798549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 987
82.2%
1 213
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 987
82.2%
1 213
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 987
82.2%
1 213
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 987
82.2%
1 213
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 987
82.2%
1 213
 
17.8%

Interactions

2023-07-01T12:18:48.884536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.015173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.431988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.637764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.960014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.282199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.516823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.105994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.437140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.678693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.960828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.250312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.928125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.236037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.567373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.977536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.111629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.526450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.732511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.048881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.371986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.605679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.198174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.521836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.767264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.049494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.338053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.021777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.327964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.660471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.067183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.207842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.619169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.820061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.136579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.453540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.691997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.285482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.607715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.853464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.137941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.424754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.115592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.417771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.752637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.158467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.302619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.712057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.909753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.223094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.542384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.779976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.373163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.691832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.939582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.225983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.511377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.204593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.509473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.843045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.245314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.394981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.803427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.004497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.309321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.627246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.864954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.458623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.773948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.022997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.312212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.597144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.288025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.596213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.930635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.325197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.481122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.886422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.085528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.390504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.706169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.942083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.539503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.848515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.101868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.389869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.673405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.367246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.677050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.011532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.415615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.580533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:31.854131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.174259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.481833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.787465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.028905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.631639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.934265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.192096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.478763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.760590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.455612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.767023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.103837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.506951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.680443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:31.941970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.264361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.569038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.871287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.115539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.720853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.018497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.280612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.566984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.847370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.543774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.856734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.196076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.586067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.767435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.020738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.344143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.650457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.943811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.194784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.803705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.093519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.357470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.645427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.924880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.622539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.937639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.276337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.672399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.861219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.104543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.429111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.739024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.023612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.279500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.893551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.175705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.440740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.730428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.011543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.711804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.024863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.361279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.760277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:29.955864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.193676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.516377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.831379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.105459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.671235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.986109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.260313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.526949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.818704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.099551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.799266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.116425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.449975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.845409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.047684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.281575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.600635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:34.921986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.186484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.757020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.075339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.343476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.613019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.904185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.182891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.884636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.204854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.534168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:49.929910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.140728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.365431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.688285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.010029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.266617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.840899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.162374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.425586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.696375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:42.987081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.269592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:45.969892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.293087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.619694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:50.017204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.237220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.456388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.778866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.102629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.349417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:37.928349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.252972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.509540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.785288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.075142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.361549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.059237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.383630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.707793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:50.106095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:30.334074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:32.547891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:33.867928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:35.192416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:36.432163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:38.016341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:39.345186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:40.593898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:41.870502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:43.161901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:44.835470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:46.146691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:47.475809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-01T12:18:48.794809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-01T12:18:56.884983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idAgeDailyRateDistanceFromHomeEmployeeNumberHourlyRateMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerBusinessTravelDepartmentEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusOverTimePerformanceRatingRelationshipSatisfactionStockOptionLevelWorkLifeBalanceAttrition
id1.0000.004-0.021-0.0800.023-0.0570.0100.0320.048-0.011-0.009-0.029-0.017-0.023-0.0150.0000.0530.0500.0330.0400.0600.0190.0260.0300.0350.0000.0000.0100.0000.0250.0490.065
Age0.0041.0000.1060.0030.110-0.0200.0910.1910.0280.2170.0770.0640.0610.0520.1100.1290.0800.0570.1260.0230.1420.0810.1700.1250.0870.1080.0440.0390.1000.0740.0810.203
DailyRate-0.0210.1061.0000.026-0.002-0.0600.0190.0520.0550.0140.012-0.079-0.058-0.107-0.0250.0890.1170.1080.1100.1160.0750.0570.1100.0680.1310.0930.0000.0280.0530.1110.1120.038
DistanceFromHome-0.0800.0030.0261.000-0.012-0.004-0.1020.055-0.040-0.019-0.0720.0260.054-0.0200.0000.0880.0630.0500.0650.0730.0100.0510.1070.0910.1140.1030.0400.0770.0970.1140.1180.111
EmployeeNumber0.0230.110-0.002-0.0121.000-0.0730.0810.110-0.0070.1160.0110.0550.0630.0770.0660.0950.1000.0600.0710.0780.1930.1010.1920.1210.0660.1000.0650.1070.1080.0470.0610.246
HourlyRate-0.057-0.020-0.060-0.004-0.0731.0000.0280.042-0.0430.079-0.0060.0490.036-0.0130.0460.0600.1070.0480.0750.1110.1100.1140.0840.0700.0980.1640.0550.0000.0800.1220.0960.140
MonthlyIncome0.0100.0910.019-0.1020.0810.0281.0000.0600.0140.0720.040-0.022-0.028-0.012-0.0040.0810.0990.0930.0630.0850.1160.0510.1580.1060.0540.1060.1020.0000.1080.1330.0750.000
NumCompaniesWorked0.0320.1910.0520.0550.1100.0420.0601.0000.0130.1580.041-0.043-0.109-0.043-0.0660.0650.1160.0910.1020.0770.1280.1510.1530.1210.1120.0950.0990.1000.1070.0430.0830.170
PercentSalaryHike0.0480.0280.055-0.040-0.007-0.0430.0140.0131.0000.057-0.0200.0000.019-0.0440.0490.0840.1650.0730.1050.1070.1170.1070.1300.1260.0990.1160.1850.8070.1930.1380.1180.157
TotalWorkingYears-0.0110.2170.014-0.0190.1160.0790.0720.1580.0571.0000.0260.2760.2280.0980.2990.0660.1430.0680.1170.0950.0670.1030.2490.1990.0980.1300.1160.1580.0880.0950.1000.191
TrainingTimesLastYear-0.0090.0770.012-0.0720.011-0.0060.0400.041-0.0200.0261.0000.014-0.000-0.003-0.0220.0870.0780.0560.0730.1110.0480.1000.0990.1080.1310.0890.0890.1250.1090.0590.1100.000
YearsAtCompany-0.0290.064-0.0790.0260.0550.049-0.022-0.0430.0000.2760.0141.0000.5580.3070.4780.1390.1190.0680.1010.0860.1420.0850.2110.1520.0960.0950.0720.0000.0440.0790.0920.140
YearsInCurrentRole-0.0170.061-0.0580.0540.0630.036-0.028-0.1090.0190.228-0.0000.5581.0000.3690.4860.1230.1050.0570.1140.0710.1440.0910.1580.1190.1030.1140.1600.1130.0960.0990.0900.264
YearsSinceLastPromotion-0.0230.052-0.107-0.0200.077-0.013-0.012-0.043-0.0440.098-0.0030.3070.3691.0000.3620.1140.0420.0720.0790.1030.0680.1020.1410.0750.1080.0810.0220.0390.0880.0730.0700.103
YearsWithCurrManager-0.0150.110-0.0250.0000.0660.046-0.004-0.0660.0490.299-0.0220.4780.4860.3621.0000.1170.0850.0780.0890.0880.0740.0820.1750.1290.0660.1540.0760.0200.0960.0960.1110.260
BusinessTravel0.0000.1290.0890.0880.0950.0600.0810.0650.0840.0660.0870.1390.1230.1140.1171.0000.1300.1210.0480.0680.1730.0800.0990.1560.0820.0270.1070.0280.0130.0660.0830.119
Department0.0530.0800.1170.0630.1000.1070.0990.1160.1650.1430.0780.1190.1050.0420.0850.1301.0000.0710.3720.0770.0950.0500.2130.7720.1060.0000.0550.0760.0840.0310.1520.109
Education0.0500.0570.1080.0500.0600.0480.0930.0910.0730.0680.0560.0680.0570.0720.0780.1210.0711.0000.1070.0480.0760.0940.0960.0980.0900.0710.0000.0940.1170.0500.0610.093
EducationField0.0330.1260.1100.0650.0710.0750.0630.1020.1050.1170.0730.1010.1140.0790.0890.0480.3720.1071.0000.1140.1050.0940.1310.2690.0880.0750.0350.0500.0890.1140.0960.103
EnvironmentSatisfaction0.0400.0230.1160.0730.0780.1110.0850.0770.1070.0950.1110.0860.0710.1030.0880.0680.0770.0480.1141.0000.0350.1160.0890.1200.1220.0910.0190.0810.0480.0950.0690.130
Gender0.0600.1420.0750.0100.1930.1100.1160.1280.1170.0670.0480.1420.1440.0680.0740.1730.0950.0760.1050.0351.0000.1160.1650.1860.0680.0580.0000.0460.0000.1150.0880.000
JobInvolvement0.0190.0810.0570.0510.1010.1140.0510.1510.1070.1030.1000.0850.0910.1020.0820.0800.0500.0940.0940.1160.1161.0000.0750.0700.0310.0190.0560.0820.0630.0790.0690.000
JobLevel0.0260.1700.1100.1070.1920.0840.1580.1530.1300.2490.0990.2110.1580.1410.1750.0990.2130.0960.1310.0890.1650.0751.0000.4930.1110.0510.1280.0160.0750.0910.1110.208
JobRole0.0300.1250.0680.0910.1210.0700.1060.1210.1260.1990.1080.1520.1190.0750.1290.1560.7720.0980.2690.1200.1860.0700.4931.0000.0840.1020.1090.0700.0910.0950.1610.245
JobSatisfaction0.0350.0870.1310.1140.0660.0980.0540.1120.0990.0980.1310.0960.1030.1080.0660.0820.1060.0900.0880.1220.0680.0310.1110.0841.0000.0800.0370.0000.1020.0640.0790.042
MaritalStatus0.0000.1080.0930.1030.1000.1640.1060.0950.1160.1300.0890.0950.1140.0810.1540.0270.0000.0710.0750.0910.0580.0190.0510.1020.0801.0000.0000.0820.1040.4180.0890.152
OverTime0.0000.0440.0000.0400.0650.0550.1020.0990.1850.1160.0890.0720.1600.0220.0760.1070.0550.0000.0350.0190.0000.0560.1280.1090.0370.0001.0000.0630.0670.0850.0400.196
PerformanceRating0.0100.0390.0280.0770.1070.0000.0000.1000.8070.1580.1250.0000.1130.0390.0200.0280.0760.0940.0500.0810.0460.0820.0160.0700.0000.0820.0631.0000.1520.0000.0560.000
RelationshipSatisfaction0.0000.1000.0530.0970.1080.0800.1080.1070.1930.0880.1090.0440.0960.0880.0960.0130.0840.1170.0890.0480.0000.0630.0750.0910.1020.1040.0670.1521.0000.1120.0780.078
StockOptionLevel0.0250.0740.1110.1140.0470.1220.1330.0430.1380.0950.0590.0790.0990.0730.0960.0660.0310.0500.1140.0950.1150.0790.0910.0950.0640.4180.0850.0000.1121.0000.0690.177
WorkLifeBalance0.0490.0810.1120.1180.0610.0960.0750.0830.1180.1000.1100.0920.0900.0700.1110.0830.1520.0610.0960.0690.0880.0690.1110.1610.0790.0890.0400.0560.0780.0691.0000.017
Attrition0.0650.2030.0380.1110.2460.1400.0000.1700.1570.1910.0000.1400.2640.1030.2600.1190.1090.0930.1030.1300.0000.0000.2080.2450.0420.1520.1960.0000.0780.1770.0171.000

Missing values

2023-07-01T12:18:50.265465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-01T12:18:50.614551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
0026Travel_Rarely450.941476Research & Development7.6010743Medical12914Male4321Laboratory Technician2Single163071YNo1333800132118700
1347Travel_Rarely730.235896Research & Development26.7394893Technical Degree15872Male9821Research Scientist1Divorced95268YYes1133801201159440
2726Travel_Rarely1082.560066Human Resources7.3747393Life Sciences15912Male8421Human Resources2Married105968YNo183280143332020
31046Travel_Rarely706.247579Research & Development14.7913731Medical15721Female7921Laboratory Technician3Divorced57620YYes204480160154771
41125Travel_Rarely500.610860Research & Development2.1469664Medical9812Male9832Manufacturing Director3Divorced170686YYes143280183310000
51236Non-Travel1025.521404Human Resources1.6895703Life Sciences10271Male4835Research Scientist2Divorced52282YNo163380122332220
61525Travel_Rarely810.789599Research & Development8.4558521Life Sciences14204Female7631Research Scientist4Married64341YYes1134801102353080
72037Travel_Frequently672.328336Research & Development28.5935545Medical6354Male3432Sales Executive3Married49361YNo2042802173432030
82137Travel_Rarely350.271536Sales25.3283353Technical Degree17982Female5712Sales Executive2Single20118YYes224380063332201
92317Travel_Rarely517.111386Sales6.3429312Marketing283Male8521Sales Representative3Single64341YYes243180042322021
idAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
1190197646Travel_Rarely390.878064Sales2.5159965Technical Degree1202Male6432Sales Executive2Married56771YNo2232800113398030
1191198018Travel_Rarely867.979366Sales7.4864534Medical16691Male4831Sales Representative3Married55614YYes193180142212001
1192198418Travel_Rarely763.515135Sales6.2288642Medical11381Male4521Sales Representative1Divorced23761YNo133480012252121
1193198634Travel_Rarely1025.887963Sales2.0755204Marketing2073Female3032Sales Executive1Divorced67810YNo1933800102367170
1194198737Non-Travel416.576646Research & Development1.6609323Medical10713Male7932Research Director3Single47891YNo173280053353130
1195198849Travel_Rarely969.251891Research & Development13.2435792Medical15874Male6735Research Director4Married50988YNo1832800333232120
1196199437Travel_Frequently437.940367Research & Development2.3394381Other1374Male8232Manufacturing Director4Married64341YYes113480065292080
1197199640Non-Travel978.883360Human Resources10.2149793Life Sciences15873Male4023Healthcare Representative3Divorced33391YNo12328011923149990
1198199737Travel_Frequently170.494984Sales2.6398792Marketing1201Male9342Sales Executive4Divorced20110YNo163180265332030
1199199826Travel_Rarely72.733977Research & Development28.0160882Life Sciences2151Female9722Healthcare Representative2Divorced47892YNo123180172222221